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    "# Creating and manipulating monte carlo spectra using MITK-MSI\n",
    "\n",
    "In this tutorial we will learn how to\n",
    "1. create reflectance spectra from examplary tissues\n",
    "2. how to analyse and visualize the created spectra\n",
    "3. how to manipulate them\n",
    "\n",
    "The MITK-MSI software provides a wrapper to the popular MCML approach to simulate how light travels through tissue. This wrapper can be found in mc/sim.py.\n",
    "In this tutorial we will utilize our tissue model which uses this wrapper to create the reflectance spectra.\n",
    "\n",
    "As a prerequisit, you need a MCML monte carlo simulation which uses the format specified [here](http://omlc.org/software/mc/).\n",
    "I tested this software with the GPU accelerated version which can be found [here](https://code.google.com/archive/p/gpumcml/)."
   ]
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   "source": [
    "# 1.1 create spectra - setup simulation environment\n",
    "\n",
    "# some necessary imports\n",
    "import logging\n",
    "import numpy as np\n",
    "import os\n",
    "# everything related to the simulation wrapper\n",
    "from mc import sim\n",
    "# the factories create batches (tissue samples) and suited tissue models\n",
    "from mc import factories\n",
    "# function which runs simulations for each wavelength\n",
    "from mc.create_spectrum import create_spectrum\n",
    "\n",
    "# Where does your monte carlo simulation executable resides in?\n",
    "MCML_EXECUTABLE = \"/home/wirkert/workspace/monteCarlo/gpumcml/fast-gpumcml/gpumcml.sm_20\"\n",
    "# The MCML needs a simulation input file, where shall it be created?\n",
    "MCI_FILENAME = \"./temp.mci\"\n",
    "# filename of the file with the simulation results. Due to a bug in GPUMCML will always\n",
    "# be created in the same folder as the MCML executable\n",
    "MCO_FILENAME = \"temp.mco\"\n",
    "# The wavelengths for which we want to run our simulation\n",
    "WAVELENGTHS = np.arange(450, 720, 2) * 10 ** -9\n",
    "\n",
    "# we want to create standard colonic tissue as specified in the IPCAI 2016 publication\n",
    "# \"Robust Near Real-Time Estimation of Physiological Parameters from Megapixel\n",
    "# Multispectral Images with Inverse Monte Carlo and Random Forest Regression\"\n",
    "factory = factories.ColonMuscleMeanScatteringFactory()\n",
    "# if you want to create data from the generic tissue mentioned in the paper, choose:\n",
    "#factory = factories.GenericMeanScatteringFactory()\n",
    "\n",
    "# create a simulation wrapper\n",
    "# the simulation wrapper wraps the mcml executable in python code\n",
    "sim_wrapper = sim.SimWrapper()\n",
    "# our simulation needs to know where the input file for the simulation\n",
    "# shall resign (will be automatically created)\n",
    "sim_wrapper.set_mci_filename(MCI_FILENAME)\n",
    "# also it needs to know where the simulation executable shall lie in\n",
    "sim_wrapper.set_mcml_executable(MCML_EXECUTABLE)\n",
    "\n",
    "# create the tissue model\n",
    "# it is responsible for writing the simulation input file\n",
    "tissue_model = factory.create_tissue_model()\n",
    "# tell it where the input file shall lie in\n",
    "tissue_model.set_mci_filename(sim_wrapper.mci_filename)\n",
    "# also set the output filename\n",
    "tissue_model.set_mco_filename(MCO_FILENAME)\n",
    "# tell it how much photons shall be simulated. Will be set to 10**6 by standard,\n",
    "# this is just an example\n",
    "tissue_model.set_nr_photons(10**6)"
   ]
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">layer0</th>\n",
       "      <th colspan=\"7\" halign=\"left\">layer1</th>\n",
       "      <th colspan=\"7\" halign=\"left\">layer2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>vhb</th>\n",
       "      <th>sao2</th>\n",
       "      <th>a_mie</th>\n",
       "      <th>b_mie</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>g</th>\n",
       "      <th>vhb</th>\n",
       "      <th>sao2</th>\n",
       "      <th>a_mie</th>\n",
       "      <th>...</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>g</th>\n",
       "      <th>vhb</th>\n",
       "      <th>sao2</th>\n",
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       "      <th>b_mie</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>g</th>\n",
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       "      <td>0.000907</td>\n",
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       "      <td>0.872626</td>\n",
       "      <td>0.068985</td>\n",
       "      <td>0.447583</td>\n",
       "      <td>912.591151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.948700</td>\n",
       "      <td>0.034427</td>\n",
       "      <td>0.447583</td>\n",
       "      <td>3150.494032</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.804610</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.021198</td>\n",
       "      <td>0.184410</td>\n",
       "      <td>3022.116532</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000782</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.930268</td>\n",
       "      <td>0.063804</td>\n",
       "      <td>0.184410</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.830601</td>\n",
       "      <td>0.028247</td>\n",
       "      <td>0.184410</td>\n",
       "      <td>3101.261499</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000430</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.813056</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1.286</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.924933</td>\n",
       "      <td>0.021053</td>\n",
       "      <td>0.307445</td>\n",
       "      <td>1433.788625</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.807348</td>\n",
       "      <td>0.006614</td>\n",
       "      <td>0.307445</td>\n",
       "      <td>1002.536901</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.943837</td>\n",
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       "      <td>1599.038671</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.900637</td>\n",
       "      <td>0.084504</td>\n",
       "      <td>0.471304</td>\n",
       "      <td>3228.771326</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>0.043674</td>\n",
       "      <td>0.471304</td>\n",
       "      <td>1810.114303</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.868953</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.053123</td>\n",
       "      <td>0.108137</td>\n",
       "      <td>3524.480885</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.833434</td>\n",
       "      <td>0.095382</td>\n",
       "      <td>0.108137</td>\n",
       "      <td>1988.134950</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000643</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.920940</td>\n",
       "      <td>0.031201</td>\n",
       "      <td>0.108137</td>\n",
       "      <td>1731.751283</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.906191</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.009147</td>\n",
       "      <td>0.866703</td>\n",
       "      <td>372.754799</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.841274</td>\n",
       "      <td>0.094915</td>\n",
       "      <td>0.866703</td>\n",
       "      <td>1660.695761</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.856969</td>\n",
       "      <td>0.009274</td>\n",
       "      <td>0.866703</td>\n",
       "      <td>1991.390563</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.813544</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.082903</td>\n",
       "      <td>0.206032</td>\n",
       "      <td>2314.415411</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.884492</td>\n",
       "      <td>0.038608</td>\n",
       "      <td>0.206032</td>\n",
       "      <td>459.458466</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.850574</td>\n",
       "      <td>0.063746</td>\n",
       "      <td>0.206032</td>\n",
       "      <td>2728.820678</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.801766</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.040719</td>\n",
       "      <td>0.200956</td>\n",
       "      <td>1858.910121</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000772</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.884516</td>\n",
       "      <td>0.003930</td>\n",
       "      <td>0.200956</td>\n",
       "      <td>3564.807750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000766</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.903764</td>\n",
       "      <td>0.047257</td>\n",
       "      <td>0.200956</td>\n",
       "      <td>1429.574407</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.849055</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.028810</td>\n",
       "      <td>0.729636</td>\n",
       "      <td>1751.818248</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.813391</td>\n",
       "      <td>0.007477</td>\n",
       "      <td>0.729636</td>\n",
       "      <td>1388.286116</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.914125</td>\n",
       "      <td>0.092993</td>\n",
       "      <td>0.729636</td>\n",
       "      <td>4001.446613</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.844552</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.089758</td>\n",
       "      <td>0.665397</td>\n",
       "      <td>3768.709188</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.836711</td>\n",
       "      <td>0.015648</td>\n",
       "      <td>0.665397</td>\n",
       "      <td>1298.629577</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000620</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.927904</td>\n",
       "      <td>0.048023</td>\n",
       "      <td>0.665397</td>\n",
       "      <td>3015.289386</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.893833</td>\n",
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       "     layer0                                                            layer1  \\\n",
       "        vhb      sao2        a_mie  b_mie         d     n         g       vhb   \n",
       "0  0.003780  0.447583  2425.900882  1.286  0.000907  1.36  0.872626  0.068985   \n",
       "1  0.021198  0.184410  3022.116532  1.286  0.000782  1.36  0.930268  0.063804   \n",
       "2  0.052096  0.307445  1171.322758  1.286  0.000897  1.36  0.924933  0.021053   \n",
       "3  0.076799  0.471304  1599.038671  1.286  0.000878  1.36  0.900637  0.084504   \n",
       "4  0.053123  0.108137  3524.480885  1.286  0.001001  1.36  0.833434  0.095382   \n",
       "5  0.009147  0.866703   372.754799  1.286  0.000943  1.36  0.841274  0.094915   \n",
       "6  0.082903  0.206032  2314.415411  1.286  0.000663  1.36  0.884492  0.038608   \n",
       "7  0.040719  0.200956  1858.910121  1.286  0.000772  1.36  0.884516  0.003930   \n",
       "8  0.028810  0.729636  1751.818248  1.286  0.000886  1.36  0.813391  0.007477   \n",
       "9  0.089758  0.665397  3768.709188  1.286  0.000800  1.36  0.836711  0.015648   \n",
       "\n",
       "                            ...                                 layer2  \\\n",
       "       sao2        a_mie    ...            d     n         g       vhb   \n",
       "0  0.447583   912.591151    ...     0.000503  1.36  0.948700  0.034427   \n",
       "1  0.184410    10.000000    ...     0.000735  1.36  0.830601  0.028247   \n",
       "2  0.307445  1433.788625    ...     0.000738  1.36  0.807348  0.006614   \n",
       "3  0.471304  3228.771326    ...     0.000605  1.36  0.905222  0.043674   \n",
       "4  0.108137  1988.134950    ...     0.000643  1.36  0.920940  0.031201   \n",
       "5  0.866703  1660.695761    ...     0.000505  1.36  0.856969  0.009274   \n",
       "6  0.206032   459.458466    ...     0.000573  1.36  0.850574  0.063746   \n",
       "7  0.200956  3564.807750    ...     0.000766  1.36  0.903764  0.047257   \n",
       "8  0.729636  1388.286116    ...     0.000813  1.36  0.914125  0.092993   \n",
       "9  0.665397  1298.629577    ...     0.000620  1.36  0.927904  0.048023   \n",
       "\n",
       "                                                           \n",
       "       sao2        a_mie  b_mie         d     n         g  \n",
       "0  0.447583  3150.494032  1.286  0.000454  1.38  0.804610  \n",
       "1  0.184410  3101.261499  1.286  0.000430  1.38  0.813056  \n",
       "2  0.307445  1002.536901  1.286  0.000485  1.38  0.943837  \n",
       "3  0.471304  1810.114303  1.286  0.000504  1.38  0.868953  \n",
       "4  0.108137  1731.751283  1.286  0.000542  1.38  0.906191  \n",
       "5  0.866703  1991.390563  1.286  0.000439  1.38  0.813544  \n",
       "6  0.206032  2728.820678  1.286  0.000494  1.38  0.801766  \n",
       "7  0.200956  1429.574407  1.286  0.000468  1.38  0.849055  \n",
       "8  0.729636  4001.446613  1.286  0.000504  1.38  0.844552  \n",
       "9  0.665397  3015.289386  1.286  0.000427  1.38  0.893833  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.2 create spectra - create tissue samples for simulation\n",
    "\n",
    "# setup batch with tissue instances which should be simulated\n",
    "batch = factory.create_batch_to_simulate()\n",
    "# we want to simulate ten tissue instances in this example\n",
    "nr_samples = 10\n",
    "df = batch.create_parameters(10)\n",
    "\n",
    "# lets have a look at the dataframe. Each row corresponds to one tissue instance,\n",
    "# each tissue instance is defined by various layers, which all have certain parameters\n",
    "# like e.g. oxygenation (here sao2)\n",
    "df"
   ]
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       "      <th>4.5e-07</th>\n",
       "      <th>4.52e-07</th>\n",
       "      <th>4.54e-07</th>\n",
       "      <th>4.56e-07</th>\n",
       "      <th>4.58e-07</th>\n",
       "      <th>4.6e-07</th>\n",
       "      <th>4.62e-07</th>\n",
       "      <th>4.64e-07</th>\n",
       "      <th>4.66e-07</th>\n",
       "      <th>4.68e-07</th>\n",
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       "      <th>7e-07</th>\n",
       "      <th>7.02e-07</th>\n",
       "      <th>7.04e-07</th>\n",
       "      <th>7.06e-07</th>\n",
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       "      <th>7.1e-07</th>\n",
       "      <th>7.12e-07</th>\n",
       "      <th>7.14e-07</th>\n",
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       "      <td>0.346943</td>\n",
       "      <td>0.371254</td>\n",
       "      <td>0.379878</td>\n",
       "      <td>0.385557</td>\n",
       "      <td>0.389087</td>\n",
       "      <td>0.394877</td>\n",
       "      <td>0.397762</td>\n",
       "      <td>0.401003</td>\n",
       "      <td>0.404130</td>\n",
       "      <td>...</td>\n",
       "      <td>0.494731</td>\n",
       "      <td>0.495162</td>\n",
       "      <td>0.495068</td>\n",
       "      <td>0.495426</td>\n",
       "      <td>0.495447</td>\n",
       "      <td>0.495008</td>\n",
       "      <td>0.495081</td>\n",
       "      <td>0.495093</td>\n",
       "      <td>0.494623</td>\n",
       "      <td>0.494477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.140266</td>\n",
       "      <td>0.189640</td>\n",
       "      <td>0.243554</td>\n",
       "      <td>0.259792</td>\n",
       "      <td>0.275593</td>\n",
       "      <td>0.284833</td>\n",
       "      <td>0.296358</td>\n",
       "      <td>0.302311</td>\n",
       "      <td>0.308877</td>\n",
       "      <td>0.314986</td>\n",
       "      <td>...</td>\n",
       "      <td>0.446426</td>\n",
       "      <td>0.447138</td>\n",
       "      <td>0.448675</td>\n",
       "      <td>0.449469</td>\n",
       "      <td>0.450297</td>\n",
       "      <td>0.451485</td>\n",
       "      <td>0.451886</td>\n",
       "      <td>0.451767</td>\n",
       "      <td>0.452054</td>\n",
       "      <td>0.452514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.021747</td>\n",
       "      <td>0.035154</td>\n",
       "      <td>0.056293</td>\n",
       "      <td>0.065113</td>\n",
       "      <td>0.073610</td>\n",
       "      <td>0.080725</td>\n",
       "      <td>0.088940</td>\n",
       "      <td>0.094565</td>\n",
       "      <td>0.100813</td>\n",
       "      <td>0.107133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.378155</td>\n",
       "      <td>0.379112</td>\n",
       "      <td>0.379953</td>\n",
       "      <td>0.381048</td>\n",
       "      <td>0.382271</td>\n",
       "      <td>0.382699</td>\n",
       "      <td>0.384109</td>\n",
       "      <td>0.384781</td>\n",
       "      <td>0.385816</td>\n",
       "      <td>0.386681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.021931</td>\n",
       "      <td>0.031995</td>\n",
       "      <td>0.045955</td>\n",
       "      <td>0.051771</td>\n",
       "      <td>0.056990</td>\n",
       "      <td>0.061694</td>\n",
       "      <td>0.067690</td>\n",
       "      <td>0.071328</td>\n",
       "      <td>0.076024</td>\n",
       "      <td>0.080335</td>\n",
       "      <td>...</td>\n",
       "      <td>0.422478</td>\n",
       "      <td>0.424793</td>\n",
       "      <td>0.425965</td>\n",
       "      <td>0.428011</td>\n",
       "      <td>0.429414</td>\n",
       "      <td>0.430371</td>\n",
       "      <td>0.432106</td>\n",
       "      <td>0.433527</td>\n",
       "      <td>0.433773</td>\n",
       "      <td>0.435063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.069287</td>\n",
       "      <td>0.107174</td>\n",
       "      <td>0.159794</td>\n",
       "      <td>0.177421</td>\n",
       "      <td>0.196123</td>\n",
       "      <td>0.208005</td>\n",
       "      <td>0.221937</td>\n",
       "      <td>0.231352</td>\n",
       "      <td>0.239231</td>\n",
       "      <td>0.247642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.477140</td>\n",
       "      <td>0.478393</td>\n",
       "      <td>0.481133</td>\n",
       "      <td>0.483172</td>\n",
       "      <td>0.485144</td>\n",
       "      <td>0.486761</td>\n",
       "      <td>0.488254</td>\n",
       "      <td>0.490285</td>\n",
       "      <td>0.491700</td>\n",
       "      <td>0.492445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.045606</td>\n",
       "      <td>0.051312</td>\n",
       "      <td>0.058581</td>\n",
       "      <td>0.062728</td>\n",
       "      <td>0.065359</td>\n",
       "      <td>0.068912</td>\n",
       "      <td>0.073484</td>\n",
       "      <td>0.075951</td>\n",
       "      <td>0.080196</td>\n",
       "      <td>0.084404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.382621</td>\n",
       "      <td>0.381922</td>\n",
       "      <td>0.381471</td>\n",
       "      <td>0.381451</td>\n",
       "      <td>0.380481</td>\n",
       "      <td>0.380568</td>\n",
       "      <td>0.379841</td>\n",
       "      <td>0.379317</td>\n",
       "      <td>0.378714</td>\n",
       "      <td>0.378075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.027393</td>\n",
       "      <td>0.044668</td>\n",
       "      <td>0.070860</td>\n",
       "      <td>0.081027</td>\n",
       "      <td>0.091248</td>\n",
       "      <td>0.098535</td>\n",
       "      <td>0.107426</td>\n",
       "      <td>0.112543</td>\n",
       "      <td>0.118445</td>\n",
       "      <td>0.123929</td>\n",
       "      <td>...</td>\n",
       "      <td>0.371301</td>\n",
       "      <td>0.373190</td>\n",
       "      <td>0.375097</td>\n",
       "      <td>0.377324</td>\n",
       "      <td>0.379127</td>\n",
       "      <td>0.380524</td>\n",
       "      <td>0.382481</td>\n",
       "      <td>0.383658</td>\n",
       "      <td>0.385444</td>\n",
       "      <td>0.386918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.048908</td>\n",
       "      <td>0.078857</td>\n",
       "      <td>0.124928</td>\n",
       "      <td>0.142237</td>\n",
       "      <td>0.159621</td>\n",
       "      <td>0.172280</td>\n",
       "      <td>0.186836</td>\n",
       "      <td>0.196870</td>\n",
       "      <td>0.206029</td>\n",
       "      <td>0.216111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.494583</td>\n",
       "      <td>0.496394</td>\n",
       "      <td>0.497351</td>\n",
       "      <td>0.498217</td>\n",
       "      <td>0.499010</td>\n",
       "      <td>0.499452</td>\n",
       "      <td>0.501481</td>\n",
       "      <td>0.501600</td>\n",
       "      <td>0.502315</td>\n",
       "      <td>0.503171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.086791</td>\n",
       "      <td>0.105141</td>\n",
       "      <td>0.125877</td>\n",
       "      <td>0.135923</td>\n",
       "      <td>0.143073</td>\n",
       "      <td>0.151115</td>\n",
       "      <td>0.160239</td>\n",
       "      <td>0.165631</td>\n",
       "      <td>0.173878</td>\n",
       "      <td>0.180887</td>\n",
       "      <td>...</td>\n",
       "      <td>0.516076</td>\n",
       "      <td>0.516351</td>\n",
       "      <td>0.517303</td>\n",
       "      <td>0.517044</td>\n",
       "      <td>0.516727</td>\n",
       "      <td>0.516664</td>\n",
       "      <td>0.515435</td>\n",
       "      <td>0.515228</td>\n",
       "      <td>0.515323</td>\n",
       "      <td>0.514933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.071479</td>\n",
       "      <td>0.089041</td>\n",
       "      <td>0.097041</td>\n",
       "      <td>0.103308</td>\n",
       "      <td>0.109447</td>\n",
       "      <td>0.116874</td>\n",
       "      <td>0.120901</td>\n",
       "      <td>0.127804</td>\n",
       "      <td>0.133711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.506670</td>\n",
       "      <td>0.506889</td>\n",
       "      <td>0.507858</td>\n",
       "      <td>0.508143</td>\n",
       "      <td>0.508351</td>\n",
       "      <td>0.508218</td>\n",
       "      <td>0.509040</td>\n",
       "      <td>0.509576</td>\n",
       "      <td>0.508506</td>\n",
       "      <td>0.508549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   4.500000e-07  4.520000e-07  4.540000e-07  4.560000e-07  4.580000e-07  \\\n",
       "0      0.318616      0.346943      0.371254      0.379878      0.385557   \n",
       "1      0.140266      0.189640      0.243554      0.259792      0.275593   \n",
       "2      0.021747      0.035154      0.056293      0.065113      0.073610   \n",
       "3      0.021931      0.031995      0.045955      0.051771      0.056990   \n",
       "4      0.069287      0.107174      0.159794      0.177421      0.196123   \n",
       "5      0.045606      0.051312      0.058581      0.062728      0.065359   \n",
       "6      0.027393      0.044668      0.070860      0.081027      0.091248   \n",
       "7      0.048908      0.078857      0.124928      0.142237      0.159621   \n",
       "8      0.086791      0.105141      0.125877      0.135923      0.143073   \n",
       "9      0.056338      0.071479      0.089041      0.097041      0.103308   \n",
       "\n",
       "   4.600000e-07  4.620000e-07  4.640000e-07  4.660000e-07  4.680000e-07  \\\n",
       "0      0.389087      0.394877      0.397762      0.401003      0.404130   \n",
       "1      0.284833      0.296358      0.302311      0.308877      0.314986   \n",
       "2      0.080725      0.088940      0.094565      0.100813      0.107133   \n",
       "3      0.061694      0.067690      0.071328      0.076024      0.080335   \n",
       "4      0.208005      0.221937      0.231352      0.239231      0.247642   \n",
       "5      0.068912      0.073484      0.075951      0.080196      0.084404   \n",
       "6      0.098535      0.107426      0.112543      0.118445      0.123929   \n",
       "7      0.172280      0.186836      0.196870      0.206029      0.216111   \n",
       "8      0.151115      0.160239      0.165631      0.173878      0.180887   \n",
       "9      0.109447      0.116874      0.120901      0.127804      0.133711   \n",
       "\n",
       "       ...       7.000000e-07  7.020000e-07  7.040000e-07  7.060000e-07  \\\n",
       "0      ...           0.494731      0.495162      0.495068      0.495426   \n",
       "1      ...           0.446426      0.447138      0.448675      0.449469   \n",
       "2      ...           0.378155      0.379112      0.379953      0.381048   \n",
       "3      ...           0.422478      0.424793      0.425965      0.428011   \n",
       "4      ...           0.477140      0.478393      0.481133      0.483172   \n",
       "5      ...           0.382621      0.381922      0.381471      0.381451   \n",
       "6      ...           0.371301      0.373190      0.375097      0.377324   \n",
       "7      ...           0.494583      0.496394      0.497351      0.498217   \n",
       "8      ...           0.516076      0.516351      0.517303      0.517044   \n",
       "9      ...           0.506670      0.506889      0.507858      0.508143   \n",
       "\n",
       "   7.080000e-07  7.100000e-07  7.120000e-07  7.140000e-07  7.160000e-07  \\\n",
       "0      0.495447      0.495008      0.495081      0.495093      0.494623   \n",
       "1      0.450297      0.451485      0.451886      0.451767      0.452054   \n",
       "2      0.382271      0.382699      0.384109      0.384781      0.385816   \n",
       "3      0.429414      0.430371      0.432106      0.433527      0.433773   \n",
       "4      0.485144      0.486761      0.488254      0.490285      0.491700   \n",
       "5      0.380481      0.380568      0.379841      0.379317      0.378714   \n",
       "6      0.379127      0.380524      0.382481      0.383658      0.385444   \n",
       "7      0.499010      0.499452      0.501481      0.501600      0.502315   \n",
       "8      0.516727      0.516664      0.515435      0.515228      0.515323   \n",
       "9      0.508351      0.508218      0.509040      0.509576      0.508506   \n",
       "\n",
       "   7.180000e-07  \n",
       "0      0.494477  \n",
       "1      0.452514  \n",
       "2      0.386681  \n",
       "3      0.435063  \n",
       "4      0.492445  \n",
       "5      0.378075  \n",
       "6      0.386918  \n",
       "7      0.503171  \n",
       "8      0.514933  \n",
       "9      0.508549  \n",
       "\n",
       "[10 rows x 135 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.3 create spectra - run simulation\n",
    "\n",
    "# add reflectance column to dataframe\n",
    "for w in WAVELENGTHS:\n",
    "    df[\"reflectances\", w] = np.NAN # the reflectances have not been calculated yet, thus set no nan\n",
    "\n",
    "# for each instance in our batch\n",
    "for i in range(df.shape[0]):\n",
    "    # set the desired element in the dataframe to be simulated\n",
    "    tissue_model.set_dataframe_row(df.loc[i, :])\n",
    "    logging.info(\"running simulation \" + str(i) + \" for\\n\" +\n",
    "                 str(tissue_model))\n",
    "    reflectances = create_spectrum(tissue_model, sim_wrapper, WAVELENGTHS)\n",
    "    # store in dataframe\n",
    "    for r, w in zip(reflectances, WAVELENGTHS):\n",
    "        df[\"reflectances\", w][i] = r\n",
    "        \n",
    "# clean up temporarily created files\n",
    "os.remove(MCI_FILENAME)\n",
    "created_mco_file = os.path.join(os.path.split(MCML_EXECUTABLE)[0], MCO_FILENAME)\n",
    "if os.path.isfile(created_mco_file):\n",
    "    os.remove(created_mco_file)\n",
    "\n",
    "# Hooray, finished,\n",
    "# now our dataframe also contains reflectances for each tissue instance:\n",
    "df[\"reflectances\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GVmdmsiYri0P5+fTy8GCIlxf3BAXxZceOtCylxl58HYy1vhIXF8c333yDi4sL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v3uO8yePRszZ86Em5sbAMBqNSE29na4uYUgPPyT6ot4xsVpFdGnT9fCDCrerxrKyrSaokuXAlu2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\nwooZFMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c563e9090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.1 analyse spectra - plot reflectances\n",
    "\n",
    "# the usual settings for plotting in ipython notebooks\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# let's have a look at our reflectances\n",
    "df[\"reflectances\"].T.plot(kind=\"line\")\n",
    "plt.ylabel(\"reflectance\")\n",
    "plt.xlabel(\"wavelengths [m]\")\n",
    "# put legend outside of plot\n",
    "plt.gca().legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IfAV4dd8EfRqAQSBpTWmmB7myqnalN7FfVdWb2q5rLfPOYkkjk2S/JH+R5MYk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\npI4zCCSp4wwCSeq4/w/frNXyhR2k8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c7c155810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.1 analyse spectra - show distribution of blood volume fraction (vhb) and sao2\n",
    "\n",
    "# now we need some special pandas functions\n",
    "import pandas as pd\n",
    "\n",
    "# we're interested in the distribution of vhb and sao2 in the first layer (layer0)\n",
    "df_vhb_sao2 = df[\"layer0\"][[\"vhb\", \"sao2\"]]\n",
    "# plot a scatter matrix showing the distribution of vhb and sao2.\n",
    "# of course, with this little data this does not really make sense,\n",
    "# however it is a useful tool for analysis if much data is available\n",
    "pd.tools.plotting.scatter_matrix(df_vhb_sao2, alpha=0.75, figsize=(6, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ktJktW9Ss4N69SqD16aN++J95Rtmdym3uMpvVBZYvhxUrlHHulltg+HD44gs1n2lHcnJg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\nmtwEbHe+K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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c5609dad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3.1 manipulate spectra - apply sliding average\n",
    "# in 3.1 and 3.2 we will adapt the generated spectra to an imaginary imaging system\n",
    "# This system has filters with 20nm bandwith (taken care of in 3.1)\n",
    "# and takes multispectral images in 10nm steps (taken care of in 3.2)\n",
    "\n",
    "# the module mc.dfmanipulations was written to provide some basic,\n",
    "# often needed manipulations of the calculated spectra\n",
    "# all dmfmanipulations are performed inplace, however, the df is also returned.\n",
    "import mc.dfmanipulations as dfmani\n",
    "\n",
    "# first copy to not lose our original data\n",
    "df2 = df.copy()\n",
    "# We apply a sliding average to our data. This is usefull if \n",
    "# we want to see e.g. how the reflectance was recorded by bands with a certain width\n",
    "# a sliding average of 11 will take the five left and five right of the current reflectance\n",
    "# and average. Because we take 2nm steps of reflectance in our simulation this means\n",
    "# a 20nm window.\n",
    "dfmani.fold_by_sliding_average(df2, 11)\n",
    "\n",
    "# lets again plot the reflectances\n",
    "df2[\"reflectances\"].T.plot(kind=\"line\")\n",
    "plt.ylabel(\"reflectance\")\n",
    "plt.xlabel(\"wavelengths [m]\")\n",
    "# put legend outside of plot\n",
    "plt.gca().legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.grid()\n",
    "# we can see that the bump at 560nm is \"smoother\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k2O6yScqsFLlw5ELFXLsbcI1XIfDompymaQ8Bd4vIM7nbI4CbReT5Asc0BRaJ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\n9ibrnJwcF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c55efd910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3.2 manipulate spectra - select certain wavelenghts\n",
    "\n",
    "# our imaginary imaging system takes images in 10nm steps from 470 to 660nm\n",
    "imaging_system_wavelengths = np.arange(470, 670, 10) * 10**-9\n",
    "\n",
    "df3 = df2.copy()\n",
    "dfmani.interpolate_wavelengths(df3, imaging_system_wavelengths)\n",
    "\n",
    "# let's look at the newly created reflectances\n",
    "df3[\"reflectances\"].T.plot(kind=\"line\", marker='o')\n",
    "plt.ylabel(\"reflectance\")\n",
    "plt.xlabel(\"wavelengths [m]\")\n",
    "# put legend outside of plot\n",
    "plt.gca().legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# that's it, folks. If you want, you can save the created dataframe easily to csv:\n",
    "df.to_csv(\"results.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
